Use data to solve complex problems

Learn the tools and techniques to be a leader and pioneer in the field using data science to solve complex problems. Students in the M.S. in Data Science (MSDS) program are taught by world-class faculty with industry experience, collaborate on team capstone projects sponsored by corporate and government partners, and learn the importance of ethical and responsible data use to benefit society and the common good.

Program benefits:

  • Integrated curriculum that focuses on hands-on learning
  • Team capstone project to apply what you learn in the classroom
  • Faculty experts from across disciplines and departments
  • Vibrant cohort experience fostering collaboration and network-building
  • Connections with industry leaders and hiring companies
  • University of Virginia network of 250K+ alumni worldwide
  • Professional development opportunities

We're looking to craft a community of motivated scholars who:

  • Are passionate about collaboratively solving the world’s challenges using data science 
  • Desire to apply their intellectual curiosity around data science to a broad range of compelling contexts
  • Are ready to engage with a rigorous STEM curriculum 
  • Recognize the value of diverse perspectives and strive to build and contribute to our inclusive community

Hear Program Director Jon Kropko discuss the integrated curriculum and program format designed for busy, working professionals. 

In just five semesters, you will learn essential data analytics topics like natural-language processing, machine learning, text analytics, and deep learning. The integrated curriculum combines hands-on learning with the application of sound data science principles, empowering you to solve real world problems and be a leader in the field. 

MSDS students join us from a wide range of disciplines and backgrounds, including astronomy, biology, business, computer science, engineering, economics, journalism, languages, mathematics, nursing, public policy, and statistics, among many others. A strong quantitative background is expected, as well as effective communication skills and a problem-solving mindset.

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